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for probabilistic unsupervised learning for structured biological data. The successful candidate will: Develop probabilistic factor models and scalable inference algorithms for structured biological (multi-view) high
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methodological comparison between mechanistic vector control models and macroscopic SIR-type models calibrated on serological survey data. Currently, risk indicators (e.g., R₀ estimation) derived from mosquito
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funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description In this project, we develop a new mathematical framework to efficiently model
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to the deep ocean. These canyons are the conduits for transport of land-derived materials to the ocean floor in avalanche-like events called turbidity currents. Turbidity currents transport enormous amounts
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Funding for: Competitive Worldwide Funding Funding amount 4 year University Scholarship starting 27/28 academic year. Minimum tax-free stipend at the current UKRI rate (for 2025/26 standard stipend
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. The doctoral research is part of the TIRIS Scalingup Microcoredeal project, which aims to develop micro-models with a focus on designing and fabricating models that bridge the gap between laboratory settings and
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-friendly software tool, based on state-of-the-art machine learning models [1], that can predict the outcome of HFNC therapy in individual patients with higher accuracy than current clinical indices, early in
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demonstration of a DNA-based stack data structure and recent advances in optimisation for DNA nanotechnology, the project will design, model, implement, and experimentally validate novel molecular data structures
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can upload participants' written memory responses and receive automated scores in return, without writing any code. The platform is designed to host a range of scoring models (both the models we develop
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the group research focus. (2) Curriculum vitae. (3) Master certificate (or current grades transcript in case of ongoing studies) Where to apply Website https://www.uni-kiel.de/personal/de/stellen/extern/wiss